Overveiw

This document will outline initial visualizations from co-location synthesis data. Inital questions that this data will attempt to address are as follows:

knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE
)
library(tidyverse)
library(here)
library(lubridate)
library(plotly)
# library(DT)
library(paletteer)
# library(ggbeeswarm)
library(cowplot)
# library(naniar)
# library(rgl) not working

Reading in Data

Data contains ‘orphans’.

mydata <- read_csv(here("data", "Data_total_working_for_r.csv"))
# I had to clean data again to put it in tidy format

types <- read_csv(here("data", "data_types_tidy.csv"))
# would love to be able to upload these all at once but alas...
# this is the only data without orphans now

scores <- read_csv(here("data", "data_types_scores.csv"))

scores_sub <- scores %>%
  select(first_author:other_present)

Data subsets

Subset to only look at year and types of co-location.

data_sub <- mydata %>% 
  select(first_author, year, country, aquaculture_present, wind_present, wave_present, tidal_present, other_present) %>% 
  group_by(year) %>% 
  summarize(count = n())
# wrangling to group by types of co-location

types_tidy <- types %>% 
  select(first_author, year, title, activity_present) %>% 
  mutate(activity_present = as.factor(activity_present)) %>% 
  group_by(activity_present) %>% 
  summarize(act_count = n()) %>% 
  arrange(-act_count) %>% 
  na.omit()


# trying creating a new data frame for the values so maybe the ggplot will be happier

colnames_v1 <- c("wind", "wave", "aquaculture", "tidal", "other")

types_counts <- c(types_tidy$act_count[1],
                  types_tidy$act_count[2],
                  types_tidy$act_count[3],
                  types_tidy$act_count[5],
                  types_tidy$act_count[4])


types_tidy_new <- data.frame(colnames_v1, types_counts)

Initial visualizations

# count of publications over time

g1 <- ggplot(data = data_sub, aes(x = year, y = count)) +
  geom_line(color = "darkgray", size = .7) +
  labs(x = "Year",
       y = "Count",
       title = "Publication frequency over time (years 2000-2020)") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

ggplotly(g1)
ggsave(filename = here("figures", "publications_time.jpeg"), width = 6, height = 5, units = "in")
# types of co-location

g2 <- ggplot(data = types_tidy_new, aes(x = factor(colnames_v1,levels = colnames_v1), y = types_counts)) +
  geom_bar(stat='identity', fill = "darkgray", width = 0.7) +
  labs(x = "Activity type",
       y = "Count",
         title = "Frequency of co-located activity") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

ggplotly(g2)
ggsave(filename = here("figures", "ocean_activities_bar.jpeg"), width = 6, height = 5, units = "in")

Patching together graphs of ecology, governance and socio-econ

So far the trends here seem to be consistent across the three categories.

# wrangling for ecology
scores_sub_ecol <- scores_sub %>%
  filter(ecology_score >0) 

colnames <- c("aquaculture", "tidal", "wave", "wind", "other")

ecol_col <- c(sum(scores_sub_ecol$aquaculture_present == "yes"),
              sum(scores_sub_ecol$tidal_present == "yes"),
              sum(scores_sub_ecol$wave_present == "yes"),
              sum(scores_sub_ecol$wind_present == "yes"),
              sum(scores_sub_ecol$other_present == "yes")
)

ecology_types <- data.frame(colnames, ecol_col)

# governance
scores_sub_gov <- scores_sub %>%
  filter(governance_score >0) 


gov_col <- c(sum(scores_sub_gov$aquaculture_present == "yes"),
              sum(scores_sub_gov$tidal_present == "yes"),
              sum(scores_sub_gov$wave_present == "yes"),
              sum(scores_sub_gov$wind_present == "yes"),
              sum(scores_sub_gov$other_present == "yes")
)

gov_types <- data.frame(colnames, gov_col)

# socioeconomics
scores_sub_socio <- scores_sub %>%
  filter(socioecon_score >0) 

socio_col <- c(sum(scores_sub_socio$aquaculture_present == "yes"),
              sum(scores_sub_socio$tidal_present == "yes"),
              sum(scores_sub_socio$wave_present == "yes"),
              sum(scores_sub_socio$wind_present == "yes"),
              sum(scores_sub_socio$other_present == "yes")
)

socio_types <- data.frame(colnames, socio_col)
# patching these together

plot1 <- ggplot(data = ecology_types, aes(x = colnames, y = ecol_col)) +
  geom_col(aes(fill = colnames)) +
  labs(x = "Ocean activity",
       y = "Count") +
  theme_minimal() +
  scale_fill_paletteer_d(palette = "calecopal::sierra2") +
  theme(legend.position= 'none')



plot2 <- ggplot(data = gov_types, aes(x = colnames, y = gov_col)) +
  geom_col(aes(fill = colnames)) +
  labs(x = "Ocean activity",
       y = "Count") +
  theme_minimal() +
  scale_fill_paletteer_d(palette = "calecopal::sierra2") +
  theme(legend.position= 'none')


plot3 <- ggplot(data = socio_types, aes(x = colnames, y = socio_col)) +
  geom_col(aes(fill = colnames)) +
  labs(x = "Ocean activity",
       y = "Count") +
  theme_minimal() +
  scale_fill_paletteer_d(palette = "calecopal::sierra2") +
  theme(legend.position= 'none')

plot_grid(plot1, plot2, plot3, labels = c('Ecology', 'Governance', 'Socio-economics'), label_size = 10, label_x = c(0.3, 0.3, 0.2), align = "h")

Graveyard

Old code that doesn’t work but we will always remember it fondly

# this doesn't work. reinstall xquartz.

# score_3D <- ggplot(data = scores, aes(x = ecology_score, y = goverance_score, z = socioecon_scores)) +
 # plot3d() +
  # theme_minimal()
# trying 3d plot with plotly
# this kinda works but something is wrong. PLus it looks bad
# plot_ly(scores_sub, x = ~ecology_score, y = ~governance_score, z = ~socioecon_score, type ="scatter3d", mode ="markers", color = ~aquaculture_present)
# instead of doing a 3d plot, trying to view two panes (front view of data, side view of data)
# try beeswarms?
# 
# library(viridis)
# 
# pane1 <- ggplot(data = scores_sub, aes(x = governance_score, y = ecology_score)) +
#   geom_point(aes(size = n(), color = aquaculture_present), alpha = 0.7) +
#   theme_minimal()
# 
# 
# pane2 <- ggplot(data = scores_sub, aes(x = socioecon_score, y = ecology_score)) +
#   geom_beeswarm() +
#   theme_minimal()